Working with datasets in R and the {tidyverse}
Subsetting data with {dplyr}
25th May 2021
Questions
How can I reduce my data set in rows or select only a certain set of columns?
Objectives
To be able to reduce the number of rows in a data set
To be able to select the columns wanted of a data set
In many cases, we are working with data sets that contain more data than we need, or we want to inspect certain parts of the data set before we continue. Subsetting data sets can be challenging in base R, because there is a fair bit of repetition. Repeating yourself will cost you time, both now and later, and potentially introduce some nasty bugs.
The {dplyr} package
The {dplyr} package provides a number of very useful functions for manipulating data sets in a way that will reduce the probability of making errors, and even save you some typing time. As an added bonus, you might even find the {dplyr} grammar easier to read.
We’re going to cover 6 of the most commonly used functions as well as using pipes (%>%) to combine them.
select()(covered in this session)filter()(covered in this session)arrange()(covered in this session)mutate()(covered in Day 2 session)group_by()(covered in Day 2 session)summarize()(covered in Day 3 session)
Selecting columns, part one
Let us first talk about selecting columns. In {dplyr}, the function name for selecting columns is select()! Most {tidyverse} function names for functions are inspired by English grammar, which will help us when we are writing our code.
We first need to make sure we have the tidyverse loaded and the penguins data set at hand.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()penguins <- palmerpenguins::penguinsTo select data, we must first tell select which data set we are selecting from, and then give it our selection. Here, we are asking R to select() from the penguins data set the island, species and sex columns
select(penguins, island, species, sex)## # A tibble: 344 x 3
## island species sex
## <fct> <fct> <fct>
## 1 Torgersen Adelie male
## 2 Torgersen Adelie female
## 3 Torgersen Adelie female
## 4 Torgersen Adelie <NA>
## 5 Torgersen Adelie female
## 6 Torgersen Adelie male
## 7 Torgersen Adelie female
## 8 Torgersen Adelie male
## 9 Torgersen Adelie <NA>
## 10 Torgersen Adelie <NA>
## # ... with 334 more rowsWhen we use select() we don’t need to use quotations, we write in the names directly. We can also use the numeric indexes for the column, if we are 100% certain of the order of the columns:
select(penguins, 1:3, 6)## # A tibble: 344 x 4
## species island bill_length_mm body_mass_g
## <fct> <fct> <dbl> <int>
## 1 Adelie Torgersen 39.1 3750
## 2 Adelie Torgersen 39.5 3800
## 3 Adelie Torgersen 40.3 3250
## 4 Adelie Torgersen NA NA
## 5 Adelie Torgersen 36.7 3450
## 6 Adelie Torgersen 39.3 3650
## 7 Adelie Torgersen 38.9 3625
## 8 Adelie Torgersen 39.2 4675
## 9 Adelie Torgersen 34.1 3475
## 10 Adelie Torgersen 42 4250
## # ... with 334 more rowsIn some cases, we want to remove columns, and not necessarily state all columns we want to keep. Select also allows for this by adding a minus (-) sign in front of the column name you don’t want.
select(penguins, -bill_length_mm, -bill_depth_mm)## # A tibble: 344 x 6
## species island flipper_length_mm body_mass_g sex year
## <fct> <fct> <int> <int> <fct> <int>
## 1 Adelie Torgersen 181 3750 male 2007
## 2 Adelie Torgersen 186 3800 female 2007
## 3 Adelie Torgersen 195 3250 female 2007
## 4 Adelie Torgersen NA NA <NA> 2007
## 5 Adelie Torgersen 193 3450 female 2007
## 6 Adelie Torgersen 190 3650 male 2007
## 7 Adelie Torgersen 181 3625 female 2007
## 8 Adelie Torgersen 195 4675 male 2007
## 9 Adelie Torgersen 193 3475 <NA> 2007
## 10 Adelie Torgersen 190 4250 <NA> 2007
## # ... with 334 more rowsSelecting columns, part two
These selections are quite convenient and fast! But they can be even better.
For instance, what if we want to choose all the columns with millimeter measurements? That could be quite convenient, making sure the variables we are working with have the same measurement scale.
We could of course type them all out, but the penguins data set has names that make it even easier for us, using something called tidy-selectors.
Here, we use a tidy-selector ends_with(), can you guess what it does? yes, it looks for columns that end with the string you provide it, here "mm".
select(penguins, ends_with("mm"))## # A tibble: 344 x 3
## bill_length_mm bill_depth_mm flipper_length_mm
## <dbl> <dbl> <int>
## 1 39.1 18.7 181
## 2 39.5 17.4 186
## 3 40.3 18 195
## 4 NA NA NA
## 5 36.7 19.3 193
## 6 39.3 20.6 190
## 7 38.9 17.8 181
## 8 39.2 19.6 195
## 9 34.1 18.1 193
## 10 42 20.2 190
## # ... with 334 more rowsSo convenient! There are several other tidy-selectors you can choose, which you can find here, but often people resort to three specific ones:
ends_with()- column names ending with a character string
starts_with()- column names starting with a character string
contains()- column names containing a character string
If you are working with a well named data set, these functions should make your data selecting much simpler. And if you are making your own data, you can think of such convenient naming for your data, so your work can be easier for you and others.
Lets only pick the measurements of the bill, we are not so interested in the flipper. Then we might want to change to starts_with() in stead.
select(penguins, starts_with("bill"))## # A tibble: 344 x 2
## bill_length_mm bill_depth_mm
## <dbl> <dbl>
## 1 39.1 18.7
## 2 39.5 17.4
## 3 40.3 18
## 4 NA NA
## 5 36.7 19.3
## 6 39.3 20.6
## 7 38.9 17.8
## 8 39.2 19.6
## 9 34.1 18.1
## 10 42 20.2
## # ... with 334 more rowsThe tidy selector can be combined with each other and other selectors. So you can build exactly the data you want!
select(penguins, island, species, year, starts_with("bill"))## # A tibble: 344 x 5
## island species year bill_length_mm bill_depth_mm
## <fct> <fct> <int> <dbl> <dbl>
## 1 Torgersen Adelie 2007 39.1 18.7
## 2 Torgersen Adelie 2007 39.5 17.4
## 3 Torgersen Adelie 2007 40.3 18
## 4 Torgersen Adelie 2007 NA NA
## 5 Torgersen Adelie 2007 36.7 19.3
## 6 Torgersen Adelie 2007 39.3 20.6
## 7 Torgersen Adelie 2007 38.9 17.8
## 8 Torgersen Adelie 2007 39.2 19.6
## 9 Torgersen Adelie 2007 34.1 18.1
## 10 Torgersen Adelie 2007 42 20.2
## # ... with 334 more rowsSelecting columns, part three
The last tidy-selector we’ll mention is where(). where() is a very special tidy selector, that uses logical evaluations to select the data. Let’s have a look at it in action, and see if we can explain it better that way.
Say you are running a correlation analysis. For correlations, you need all the columns in your data to be numeric, as you cannot correlate strings or categories. Going through each individual column and seeing if it is numeric is a bit of a chore. That is where where() comes in!
select(penguins, where(is.numeric))## # A tibble: 344 x 5
## bill_length_mm bill_depth_mm flipper_length_mm body_mass_g year
## <dbl> <dbl> <int> <int> <int>
## 1 39.1 18.7 181 3750 2007
## 2 39.5 17.4 186 3800 2007
## 3 40.3 18 195 3250 2007
## 4 NA NA NA NA 2007
## 5 36.7 19.3 193 3450 2007
## 6 39.3 20.6 190 3650 2007
## 7 38.9 17.8 181 3625 2007
## 8 39.2 19.6 195 4675 2007
## 9 34.1 18.1 193 3475 2007
## 10 42 20.2 190 4250 2007
## # ... with 334 more rowsMagic! Let’s break that down. is.numeric() is a function in R that checks if a vector is numeric. If the vector is numeric, it returns TRUE if not it returns FALSE.
is.numeric(5)## [1] TRUEis.numeric("something")## [1] FALSELet us look at the penguins data set again
penguins## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>The penguins data is stored as a tibble, which is a special kind of data set in R that gives a nice print out of the data. Notice, right below the column name, there is some information in <> marks. This tells us the class of the columns. Species and island are factors, while bill columns are “double” which is a decimal numeric class.
where() goes through all the columns and checks if they are numeric, and returns the ones that are. We could try the same for factors!
select(penguins, where(is.factor))## # A tibble: 344 x 3
## species island sex
## <fct> <fct> <fct>
## 1 Adelie Torgersen male
## 2 Adelie Torgersen female
## 3 Adelie Torgersen female
## 4 Adelie Torgersen <NA>
## 5 Adelie Torgersen female
## 6 Adelie Torgersen male
## 7 Adelie Torgersen female
## 8 Adelie Torgersen male
## 9 Adelie Torgersen <NA>
## 10 Adelie Torgersen <NA>
## # ... with 334 more rowsSelecting columns, challenges
Assignment
1a: Select from the penguins data set only columns that are factors.
1b: Now we lost flipper length! To make sure we keep flipper length, instead select columns what end with “mm”.
1c: Now select the columns island, species, and all numeric columns
Solution
## 1a
select(penguins, where(is.factor))## # A tibble: 344 x 3
## species island sex
## <fct> <fct> <fct>
## 1 Adelie Torgersen male
## 2 Adelie Torgersen female
## 3 Adelie Torgersen female
## 4 Adelie Torgersen <NA>
## 5 Adelie Torgersen female
## 6 Adelie Torgersen male
## 7 Adelie Torgersen female
## 8 Adelie Torgersen male
## 9 Adelie Torgersen <NA>
## 10 Adelie Torgersen <NA>
## # ... with 334 more rows# 1b
select(penguins, where(is.integer))## # A tibble: 344 x 3
## flipper_length_mm body_mass_g year
## <int> <int> <int>
## 1 181 3750 2007
## 2 186 3800 2007
## 3 195 3250 2007
## 4 NA NA 2007
## 5 193 3450 2007
## 6 190 3650 2007
## 7 181 3625 2007
## 8 195 4675 2007
## 9 193 3475 2007
## 10 190 4250 2007
## # ... with 334 more rows## 1c
select(penguins, island, species, where(is.numeric))## # A tibble: 344 x 7
## island species bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Torgersen Adelie 39.1 18.7 181 3750
## 2 Torgersen Adelie 39.5 17.4 186 3800
## 3 Torgersen Adelie 40.3 18 195 3250
## 4 Torgersen Adelie NA NA NA NA
## 5 Torgersen Adelie 36.7 19.3 193 3450
## 6 Torgersen Adelie 39.3 20.6 190 3650
## 7 Torgersen Adelie 38.9 17.8 181 3625
## 8 Torgersen Adelie 39.2 19.6 195 4675
## 9 Torgersen Adelie 34.1 18.1 193 3475
## 10 Torgersen Adelie 42 20.2 190 4250
## # ... with 334 more rows, and 1 more variable: year <int>Filtering rows, part one
Now that we know how to select the columns we want, we should take a look at how we filter the rows. Row filtering is done with the function filter(), which takes statements that can be evaluated to TRUE or FALSE.
What do we mean with statements that can be evaluated to TRUE or FALSE? In the example with where() we used the is.numeric() function to evaluate if the columns where numeric or not. We will be doing the same for rows!
Now, using is.numeric() on a row won’t help, because every row-value in a column will be of the same type, that is how the data set works. All values in a column must be of the same type.
So what can we do? Well, we can check if the values meet certain criteria or not. Like values being above 20, or factors being a specific factor.
filter(penguins, body_mass_g < 3000)## # A tibble: 9 x 8
## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex
## <fct> <fct> <dbl> <dbl> <int> <int> <fct>
## 1 Adelie Dream 37.5 18.9 179 2975 <NA>
## 2 Adelie Biscoe 34.5 18.1 187 2900 fema~
## 3 Adelie Biscoe 36.5 16.6 181 2850 fema~
## 4 Adelie Biscoe 36.4 17.1 184 2850 fema~
## 5 Adelie Dream 33.1 16.1 178 2900 fema~
## 6 Adelie Biscoe 37.9 18.6 193 2925 fema~
## 7 Adelie Torge~ 38.6 17 188 2900 fema~
## 8 Chinst~ Dream 43.2 16.6 187 2900 fema~
## 9 Chinst~ Dream 46.9 16.6 192 2700 fema~
## # ... with 1 more variable: year <int>Here, we’ve filtered so that we only have observations where the body mass was less than 3 kilos. We can also filter for specific values, but beware! you must use double equals (==) for comparisons, as single equals (=) are for argument names in functions.
filter(penguins, body_mass_g == 2900)## # A tibble: 4 x 8
## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex
## <fct> <fct> <dbl> <dbl> <int> <int> <fct>
## 1 Adelie Biscoe 34.5 18.1 187 2900 fema~
## 2 Adelie Dream 33.1 16.1 178 2900 fema~
## 3 Adelie Torge~ 38.6 17 188 2900 fema~
## 4 Chinst~ Dream 43.2 16.6 187 2900 fema~
## # ... with 1 more variable: year <int>What is happening, is that R will check if the values in body_mass_g are the same as 2900 (TRUE) or not (FALSE), and will do this for every row in the data set. Then at the end, it will discard all those that are FALSE, and keep those that are TRUE.
Filtering rows, part two
Many times, we will want to have several filters applied at once. What if you only want Adelie penguins that are below 3 kilos? filter() can take as many statements as you want! Combine them by adding commas (,) between each statement, and that will work as ‘and’.
filter(penguins,
species == "Chinstrap",
body_mass_g < 3000)## # A tibble: 2 x 8
## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex
## <fct> <fct> <dbl> <dbl> <int> <int> <fct>
## 1 Chinst~ Dream 43.2 16.6 187 2900 fema~
## 2 Chinst~ Dream 46.9 16.6 192 2700 fema~
## # ... with 1 more variable: year <int>You can also use the & sign, which in R is the comparison character for ‘and’, like == is for ‘equals’.
filter(penguins,
species == "Chinstrap" &
body_mass_g < 3000)## # A tibble: 2 x 8
## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex
## <fct> <fct> <dbl> <dbl> <int> <int> <fct>
## 1 Chinst~ Dream 43.2 16.6 187 2900 fema~
## 2 Chinst~ Dream 46.9 16.6 192 2700 fema~
## # ... with 1 more variable: year <int>Here we are filtering the penguins data set keeping only the species “Chinstrap” and those below 3.5 kilos. And we can keep going!
filter(penguins,
species == "Chinstrap",
body_mass_g < 3000,
sex == "male")## # A tibble: 0 x 8
## # ... with 8 variables: species <fct>, island <fct>, bill_length_mm <dbl>,
## # bill_depth_mm <dbl>, flipper_length_mm <int>, body_mass_g <int>, sex <fct>,
## # year <int>But what if we want all the Chinstrap penguins or if body mass is below 3 kilos? When we use the comma (or the &), we make sure that all statements are TRUE. But what if we want it so that either statement is true? Then we can use the or character | .
filter(penguins,
species == "Chinstrap" |
body_mass_g < 3000)## # A tibble: 75 x 8
## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Dream 37.5 18.9 179 2975
## 2 Adelie Biscoe 34.5 18.1 187 2900
## 3 Adelie Biscoe 36.5 16.6 181 2850
## 4 Adelie Biscoe 36.4 17.1 184 2850
## 5 Adelie Dream 33.1 16.1 178 2900
## 6 Adelie Biscoe 37.9 18.6 193 2925
## 7 Adelie Torgersen 38.6 17 188 2900
## 8 Chinstrap Dream 46.5 17.9 192 3500
## 9 Chinstrap Dream 50 19.5 196 3900
## 10 Chinstrap Dream 51.3 19.2 193 3650
## # ... with 65 more rows, and 2 more variables: sex <fct>, year <int>This now gives us both all chinstrap penguins, and the smallest Adelie penguins! By combining AND and OR statements this way, we can slowly create the filtering we are after.
Filtering rows, challenges
Assignment
2a: Using a comma (‘,’), each expression must be TRUE for the end result. Choose all data where flipper length is larger or equal to 180, and the species is “Gentoo”
2b: Do the same using the
&(and) sign.2c: Filter the penguins data so that you have either chinstrap penguins, or penguins with body mass below or equal to 3 kilos.
Solution
## 2a
filter(penguins,
flipper_length_mm >= 180,
species == "Gentoo")## # A tibble: 123 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Gentoo Biscoe 46.1 13.2 211 4500
## 2 Gentoo Biscoe 50 16.3 230 5700
## 3 Gentoo Biscoe 48.7 14.1 210 4450
## 4 Gentoo Biscoe 50 15.2 218 5700
## 5 Gentoo Biscoe 47.6 14.5 215 5400
## 6 Gentoo Biscoe 46.5 13.5 210 4550
## 7 Gentoo Biscoe 45.4 14.6 211 4800
## 8 Gentoo Biscoe 46.7 15.3 219 5200
## 9 Gentoo Biscoe 43.3 13.4 209 4400
## 10 Gentoo Biscoe 46.8 15.4 215 5150
## # ... with 113 more rows, and 2 more variables: sex <fct>, year <int>## 2b
filter(penguins,
flipper_length_mm >= 180 &
species == "Gentoo")## # A tibble: 123 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Gentoo Biscoe 46.1 13.2 211 4500
## 2 Gentoo Biscoe 50 16.3 230 5700
## 3 Gentoo Biscoe 48.7 14.1 210 4450
## 4 Gentoo Biscoe 50 15.2 218 5700
## 5 Gentoo Biscoe 47.6 14.5 215 5400
## 6 Gentoo Biscoe 46.5 13.5 210 4550
## 7 Gentoo Biscoe 45.4 14.6 211 4800
## 8 Gentoo Biscoe 46.7 15.3 219 5200
## 9 Gentoo Biscoe 43.3 13.4 209 4400
## 10 Gentoo Biscoe 46.8 15.4 215 5150
## # ... with 113 more rows, and 2 more variables: sex <fct>, year <int>## 2c
filter(penguins,
species == "Chinstrap" |
body_mass_g < 3000)## # A tibble: 75 x 8
## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Dream 37.5 18.9 179 2975
## 2 Adelie Biscoe 34.5 18.1 187 2900
## 3 Adelie Biscoe 36.5 16.6 181 2850
## 4 Adelie Biscoe 36.4 17.1 184 2850
## 5 Adelie Dream 33.1 16.1 178 2900
## 6 Adelie Biscoe 37.9 18.6 193 2925
## 7 Adelie Torgersen 38.6 17 188 2900
## 8 Chinstrap Dream 46.5 17.9 192 3500
## 9 Chinstrap Dream 50 19.5 196 3900
## 10 Chinstrap Dream 51.3 19.2 193 3650
## # ... with 65 more rows, and 2 more variables: sex <fct>, year <int>Creating subsetted objects
So far, we have kept working on the penguins data set, without actually altering it. So far, all our actions have been executed, then forgotten by R. Like it never happened. This is actually quite smart, since it makes it harder to do mistakes you can have difficulties changing.
To store the changes, we have to “assign” the data to a new object in the R environment. Like the penguins data set, which already is an object in our environment we have called “penguins”.
We will now store a filtered version including only the chinstrap penguins, in an object we call chinstraps.
chinstraps <- filter(penguins, species == "Chinstrap")You will likely notice that when we execute this command, nothing is output to the console. That is expected. When we assign the output of a function somewhere, and everything works (i.e., no errors), nothing happens in the console.
But you should be able to see the new chinstraps object in your environment, and when we type chinstraps in the R console, it prints our chinstraps data.
chinstraps## # A tibble: 68 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Chinstrap Dream 46.5 17.9 192 3500
## 2 Chinstrap Dream 50 19.5 196 3900
## 3 Chinstrap Dream 51.3 19.2 193 3650
## 4 Chinstrap Dream 45.4 18.7 188 3525
## 5 Chinstrap Dream 52.7 19.8 197 3725
## 6 Chinstrap Dream 45.2 17.8 198 3950
## 7 Chinstrap Dream 46.1 18.2 178 3250
## 8 Chinstrap Dream 51.3 18.2 197 3750
## 9 Chinstrap Dream 46 18.9 195 4150
## 10 Chinstrap Dream 51.3 19.9 198 3700
## # ... with 58 more rows, and 2 more variables: sex <fct>, year <int>Maybe in this chinstrap data we are also not interested in the bill measurements, so we want to remove them.
chinstraps <- select(chinstraps, -starts_with("bill"))
chinstraps## # A tibble: 68 x 6
## species island flipper_length_mm body_mass_g sex year
## <fct> <fct> <int> <int> <fct> <int>
## 1 Chinstrap Dream 192 3500 female 2007
## 2 Chinstrap Dream 196 3900 male 2007
## 3 Chinstrap Dream 193 3650 male 2007
## 4 Chinstrap Dream 188 3525 female 2007
## 5 Chinstrap Dream 197 3725 male 2007
## 6 Chinstrap Dream 198 3950 female 2007
## 7 Chinstrap Dream 178 3250 female 2007
## 8 Chinstrap Dream 197 3750 male 2007
## 9 Chinstrap Dream 195 4150 female 2007
## 10 Chinstrap Dream 198 3700 male 2007
## # ... with 58 more rowsNow our data has two less columns, and many fewer rows. A simpler data set for us to work with. But assigning the chinstrap data twice like this is a lot of typing, and there is a simpler way, using something we call the “pipe”.
The pipe %>%
We often want to string together series of functions. This is achieved using pipe operator %>%. This takes the value on the left, and passes it as the first argument to the function call on the right.
%>% is not limited to {dplyr} functions. It’s an alternative way of writing any R code:
The shortcut to insert the pipe operator is Ctrl+Shift+M for Windows/Linux, and Cmd+Shift+M for Mac.
In the chinstraps example, we had the following code to filter the rows and then select our columns.
chinstraps <- filter(penguins, species == "Chinstrap")
chinstraps <- select(chinstraps, -starts_with("bill"))Here we first create the chinstraps data from the filtered penguins data set. Then use that chinstraps data to reduce the columns and write it again back to the same chinstraps object. It’s a little messy. With the pipe, we can make it more streamlined.
chinstraps <- penguins %>%
filter(species == "Chinstrap") %>%
select(-starts_with("bill"))The end result is the same, but there is less typing and we can “read” the pipeline of data subsetting more like language, if we know how. You can read the pipe operator as “and then”.
So if we translate the code above to human language we could read it as:
take the penguins data set, and then keep only rows for the chinstrap penguins, and then remove the columns starting with bill and assign the end result to chinstraps.
Learning to read pipes is a great skill, R is not the only programming language that can do this (though the operator is different between languages, the functionality exists in many).
We can do the entire pipe chain step by step to see what is happening.
penguins## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>penguins %>%
filter(species == "Chinstrap")## # A tibble: 68 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Chinstrap Dream 46.5 17.9 192 3500
## 2 Chinstrap Dream 50 19.5 196 3900
## 3 Chinstrap Dream 51.3 19.2 193 3650
## 4 Chinstrap Dream 45.4 18.7 188 3525
## 5 Chinstrap Dream 52.7 19.8 197 3725
## 6 Chinstrap Dream 45.2 17.8 198 3950
## 7 Chinstrap Dream 46.1 18.2 178 3250
## 8 Chinstrap Dream 51.3 18.2 197 3750
## 9 Chinstrap Dream 46 18.9 195 4150
## 10 Chinstrap Dream 51.3 19.9 198 3700
## # ... with 58 more rows, and 2 more variables: sex <fct>, year <int>penguins %>%
filter(species == "Chinstrap") %>%
select(-starts_with("bill"))## # A tibble: 68 x 6
## species island flipper_length_mm body_mass_g sex year
## <fct> <fct> <int> <int> <fct> <int>
## 1 Chinstrap Dream 192 3500 female 2007
## 2 Chinstrap Dream 196 3900 male 2007
## 3 Chinstrap Dream 193 3650 male 2007
## 4 Chinstrap Dream 188 3525 female 2007
## 5 Chinstrap Dream 197 3725 male 2007
## 6 Chinstrap Dream 198 3950 female 2007
## 7 Chinstrap Dream 178 3250 female 2007
## 8 Chinstrap Dream 197 3750 male 2007
## 9 Chinstrap Dream 195 4150 female 2007
## 10 Chinstrap Dream 198 3700 male 2007
## # ... with 58 more rowsSo, for each chain step, the output of the previous step is fed into the next step, and that way the commands build on each other until a final end result is made.
And as before, we still are seeing the output of the command chain in the console, meaning we are not storing it. Let us do that, again using the assignment.
chinstraps <- penguins %>%
filter(species == "Chinstrap") %>%
select(-starts_with("bill"))
chinstraps## # A tibble: 68 x 6
## species island flipper_length_mm body_mass_g sex year
## <fct> <fct> <int> <int> <fct> <int>
## 1 Chinstrap Dream 192 3500 female 2007
## 2 Chinstrap Dream 196 3900 male 2007
## 3 Chinstrap Dream 193 3650 male 2007
## 4 Chinstrap Dream 188 3525 female 2007
## 5 Chinstrap Dream 197 3725 male 2007
## 6 Chinstrap Dream 198 3950 female 2007
## 7 Chinstrap Dream 178 3250 female 2007
## 8 Chinstrap Dream 197 3750 male 2007
## 9 Chinstrap Dream 195 4150 female 2007
## 10 Chinstrap Dream 198 3700 male 2007
## # ... with 58 more rowsSorting rows
So far, we have looked at subsetting the data. But some times, we want to reorganize the data without altering it. In tables, we are used to be able to sort columns in ascending or descending order.
This can also be done with {dplyr}’s arrange() function. arrange does not alter the data per se, just the order in which the rows are stored.
penguins %>%
arrange(island)## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Biscoe 37.8 18.3 174 3400
## 2 Adelie Biscoe 37.7 18.7 180 3600
## 3 Adelie Biscoe 35.9 19.2 189 3800
## 4 Adelie Biscoe 38.2 18.1 185 3950
## 5 Adelie Biscoe 38.8 17.2 180 3800
## 6 Adelie Biscoe 35.3 18.9 187 3800
## 7 Adelie Biscoe 40.6 18.6 183 3550
## 8 Adelie Biscoe 40.5 17.9 187 3200
## 9 Adelie Biscoe 37.9 18.6 172 3150
## 10 Adelie Biscoe 40.5 18.9 180 3950
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>Here we have sorted the data by the island column. Since island is a factor, it will order by the facor levels, which in this case has Biscoe island as the first category. If we sort a numeric column, it will sort by numeric value.
By default, arrange sorts in ascending order. If you want it sorted by descending order, wrap the column name in desc()
penguins %>%
arrange(desc(island))## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>Sorting rows, challenges
Assignment
3a: Arrange the penguins data by body mass.
3b: Arrange the penguins data by descending order of flipper length.
3c: You can arrange on multiple columns. Try arranging the data by ascending island and descending flipper length, using a comma between the two arguments.
Solution
## 3a
penguins %>%
arrange(body_mass_g)## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Chinstrap Dream 46.9 16.6 192 2700
## 2 Adelie Biscoe 36.5 16.6 181 2850
## 3 Adelie Biscoe 36.4 17.1 184 2850
## 4 Adelie Biscoe 34.5 18.1 187 2900
## 5 Adelie Dream 33.1 16.1 178 2900
## 6 Adelie Torgersen 38.6 17 188 2900
## 7 Chinstrap Dream 43.2 16.6 187 2900
## 8 Adelie Biscoe 37.9 18.6 193 2925
## 9 Adelie Dream 37.5 18.9 179 2975
## 10 Adelie Dream 37 16.9 185 3000
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>## 3b
penguins %>%
arrange(desc(flipper_length_mm))## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Gentoo Biscoe 54.3 15.7 231 5650
## 2 Gentoo Biscoe 50 16.3 230 5700
## 3 Gentoo Biscoe 59.6 17 230 6050
## 4 Gentoo Biscoe 49.8 16.8 230 5700
## 5 Gentoo Biscoe 48.6 16 230 5800
## 6 Gentoo Biscoe 52.1 17 230 5550
## 7 Gentoo Biscoe 51.5 16.3 230 5500
## 8 Gentoo Biscoe 55.1 16 230 5850
## 9 Gentoo Biscoe 49.5 16.2 229 5800
## 10 Gentoo Biscoe 49.8 15.9 229 5950
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>## 3c
penguins %>%
arrange(island,
desc(flipper_length_mm))## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Gentoo Biscoe 54.3 15.7 231 5650
## 2 Gentoo Biscoe 50 16.3 230 5700
## 3 Gentoo Biscoe 59.6 17 230 6050
## 4 Gentoo Biscoe 49.8 16.8 230 5700
## 5 Gentoo Biscoe 48.6 16 230 5800
## 6 Gentoo Biscoe 52.1 17 230 5550
## 7 Gentoo Biscoe 51.5 16.3 230 5500
## 8 Gentoo Biscoe 55.1 16 230 5850
## 9 Gentoo Biscoe 49.5 16.2 229 5800
## 10 Gentoo Biscoe 49.8 15.9 229 5950
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int>Combining with {ggplot}
Some times, we want to subset data just to reduce the data in a plot, not necessarily for our whole pipeline. For instance, maybe you want to plot the data just from the Gentoo species, but don’t want to make another data set.
We can do that with pipes!
penguins %>%
filter(species == "Gentoo") %>%
ggplot(aes(x = bill_depth_mm,
y = bill_length_mm,
colour = species)) +
geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
Here, we first filter the penguins data, and then pass that output to ggplot with a pipe. This reduces the data immediately, without storing the intermediate.
Wrap-up
Now we’ve learned about subsetting and sorting our data, so we can create data sets that are suited to our needs. We also learned about chaining commands, the use of the pipe to create a series of commands that build on each other to create a final wanted output.